E3GCAPS: Efficient EEG-based multi-capsule framework with dynamic attention for cross-subject cognitive state detection

• Published in: China communications Vol. 19; no. 2; pp. 73 - 89
• Main Authors: Zhao, Yue, Dai, Guojun, Fang, Xin, Wu, Zhengxuan, Xia, Nianzhang, Jin, Yanping, Zeng, Hong
• Format: Journal Article
• Language: English
• Published: China Institute of Communications 01.02.2022; School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou 310018,China%School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou 310018,China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province,Hangzhou 310018,China
• Subjects: Brain modeling; capsule network; cognitive state detection; cross-subject; Electroencephalography; electroencephalography (EEG); Feature extraction; Heuristic algorithms; Routing; Task analysis; Training; electroencephalography (EEG); capsule network; cross-subject; cognitive state detection
• ISSN: 1673-5447
• DOI: 10.23919/JCC.2022.02.007

Cognitive state detection using electroencephalogram (EEG) signals for various tasks has attracted significant research attention. However, it is difficult to further improve the performance of cross-subject cognitive state detection. Further, most of the existing deep learning models will degrade significantly when limited training samples are given, and the feature hierarchical relationships are ignored. To address the above challenges, we propose an efficient interpretation model based on multiple capsule networks for cross-subject EEG cognitive state detection, termed as Efficient EEG-based Multi-Capsule Framework (E3GCAPS). Specifically, we use a self-expression module to capture the potential connections between samples, which is beneficial to alleviate the sensitivity of outliers that are caused by the individual differences of cross-subject EEG. In addition, considering the strong correlation between cognitive states and brain function connection mode, the dynamic subcapsule-based spatial attention mechanism is introduced to explore the spatial relationship of multi-channel 1D EEG data, in which multichannel 1D data greatly improving the training efficiency while preserving the model performance. The effectiveness of the E3GCAPS is validated on the Fatigue-Awake EEG Dataset (FAAD) and the SJTU Emotion EEG Dataset (SEED). Experimental results show E3GCAPS can achieve remarkable results on the EEG-based cross-subject cognitive state detection under different tasks.